The present invention relates to sentence parsing, and more specifically, to a deep learning approach to grammatical correction for incomplete parses.
Understanding natural language and finding entity relationships in text is a difficult task for computing systems. Often, computing systems rely on rich natural language text to extract information that can be used in decision making processes. One such example is extracting disease-related information from electronic medical records for use by medical logic to generate treatment recommendations. Syntactic and structural information is required to extract entity relations in natural language. Parse trees are a source of information about entity relations that can be processed. Therefore, many systems rely on parse trees to extract information about entity relations. However, if parsing of a sentence fails (e.g., a complete parse tree cannot be generated), information cannot be extracted therefrom. Furthermore, converting an incomplete parse to a complete parse is challenging.